Storage offload engine for distributed network device data

ABSTRACT

Techniques are provided for offloading the management of sensor data and generating custom views of sensor data. Sensor data received from a data network through a message is stored within storage managed by a computing device. A handle is generated to identify the sensor data. The sensor data within the message is replaced with the handle, and the message is transmitted to a device within the data network. The device may use handles of sensor data to request custom views of sensor data.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application,titled “STORAGE OFFLOAD ENGINE FOR DISTRIBUTED NETWORK DEVICE LARGEDATA”, filed on Nov. 5, 2018 and accorded Application No.: 62/755,778,which is incorporated herein by reference.

BACKGROUND

A data network, such as an Internet of Things (IoT), an IndustrialInternet of Things (IIoT), or any other network where data is beinggenerated or collected, may comprise sensors that generate sensor data.For example, a security camera may generate imagery, a vehicle maygenerate locational data, a medical device may generate health data,etc. The data network may comprise a large number of sensors that aregenerating a substantial amount of sensor data. Some sensors maygenerate large sensor data, such as videos, images, spectrum data, etc.Other sensors may be high frequency sensors that generate high frequencysensor data, such as where thousands to millions or more data samplesare acquired per second.

A distributed data processing architecture may be message based, wheremessages of data are passed between devices and components of the datanetwork. The messages may be passed using queues and are processed whenreceived. Unfortunately, this architecture has scalability, reliability,and manageability problems when receiving, processing, storing, andtransmitting large sensor data and/or high frequency sensor data. Thiswill result in increased latency of processing messages, or theinability to keep up with incoming messages.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a component block diagram illustrating an example clusterednetwork in which an embodiment of the invention may be implemented.

FIG. 2 is a component block diagram illustrating an example data storagesystem in which an embodiment of the invention may be implemented.

FIG. 3 is a flow chart illustrating an example method for offloadingmanagement of sensor data.

FIG. 4 is a flow chart illustrating an example method for providingcustom views of sensor data.

FIG. 5 is a component block diagram illustrating an example system foroffloading management of sensor data and for providing custom views ofsensor data.

FIG. 6 is an example of a computer readable medium in which anembodiment of the invention may be implemented.

FIG. 7 is a component block diagram illustrating an example computingenvironment in which an embodiment of the invention may be implemented.

DETAILED DESCRIPTION

Some examples of the claimed subject matter are now described withreference to the drawings, where like reference numerals are generallyused to refer to like elements throughout. In the following description,for purposes of explanation, numerous specific details are set forth inorder to provide an understanding of the claimed subject matter. It maybe evident, however, that the claimed subject matter may be practicedwithout these specific details. Nothing in this detailed description isadmitted as prior art.

A data network, such as an Internet of Things (IoT) or an IndustrialInternet of Things (IIoT), may comprise sensors that generate sensordata that is typically transmitted through the data network using amessage based communication architecture. The data network may beassociated with an edge network to which the sensors are connected fordata collection and aggregation (e.g., vehicles, security cameras,locational devices such as global positioning systems (GPS), medicalequipment, industrial equipment, computers, and other devices withsensors may connect to the edge network). The data network may beassociated with a core network comprising dedicated hardware andsoftware. The data network may be associated with a cloud network suchas where a hyperscaler is hosted as a service solution in the cloud toprovide longer term data storage and processing. Unfortunately, amessage-based communication software stack may be unable to adequatelystore, process, and/or transmit large sensor data and/or high frequencysensor data, thus resulting in reliability issues, increased latency,and other degraded performance issues.

Accordingly, as provided herein, storage, processing, and/ortransmission of such sensor data may be offloaded from the message-basedcommunication software stack by a storage offload engine to a separatecomputing environment, such as an offloaded storage environment ofcomputing devices executing a storage operating system, storageservices, deduplication functionality, encryption functionality,compression functionality, replication/migration functionality, and/orother storage functionality for the sensor data. Furthermore, deviceswithin the data network are provided with handles encoded withinformation relating to sensor data uniquely referenced by the handles.Thus, a device can use the handles to request a view of particularsensor data stored within the offloaded storage environment (e.g., aview of sensor data from a particular type of sensor, a view of sensordata stored in a particular location, a view of sensor data createdduring a particular timespan, etc.). Offloading the management of sensordata to the offloaded storage environment allows the offloaded storageenvironment to efficiently manage the storage, processing, andtransmission of sensor data such as on demand access to sensor data,along with providing storage functionality for the sensor data such asdeduplication, compression, encryption, mirroring, replication,migration, traceability, attestation, etc. Offloading the management ofsensor data from the data network 514 reduces burden otherwise placed onthe data network 514, and thus performance of the data network 514 willimprove (e.g., lowered latency).

To provide for offloading the management of sensor data and thegeneration of custom views of sensor data, FIG. 1 illustrates anembodiment of a clustered network environment 100 or a network storageenvironment. It may be appreciated, however, that the techniques, etc.described herein may be implemented within the clustered networkenvironment 100, a non-cluster network environment, and/or a variety ofother computing environments, such as a desktop computing environment.That is, the instant disclosure, including the scope of the appendedclaims, is not meant to be limited to the examples provided herein. Itwill be appreciated that where the same or similar components, elements,features, items, modules, etc. are illustrated in later figures but werepreviously discussed with regard to prior figures, that a similar (e.g.,redundant) discussion of the same may be omitted when describing thesubsequent figures (e.g., for purposes of simplicity and ease ofunderstanding).

FIG. 1 is a block diagram illustrating the clustered network environment100 that may implement at least some embodiments of the techniquesand/or systems described herein. The clustered network environment 100comprises data storage systems 102 and 104 that are coupled over acluster fabric 106, such as a computing network embodied as a privateInfiniband, Fibre Channel (FC), or Ethernet network facilitatingcommunication between the data storage systems 102 and 104 (and one ormore modules, component, etc. therein, such as, nodes 116 and 118, forexample). It will be appreciated that while two data storage systems 102and 104 and two nodes 116 and 118 are illustrated in FIG. 1, that anysuitable number of such components is contemplated. In an example, nodes116, 118 comprise storage controllers (e.g., node 116 may comprise aprimary or local storage controller and node 118 may comprise asecondary or remote storage controller) that provide client devices,such as host devices 108, 110, with access to data stored within datastorage devices 128, 130. Similarly, unless specifically providedotherwise herein, the same is true for other modules, elements,features, items, etc. referenced herein and/or illustrated in theaccompanying drawings. That is, a particular number of components,modules, elements, features, items, etc. disclosed herein is not meantto be interpreted in a limiting manner.

It will be further appreciated that clustered networks are not limitedto any particular geographic areas and can be clustered locally and/orremotely. Thus, In an embodiment a clustered network can be distributedover a plurality of storage systems and/or nodes located in a pluralityof geographic locations; while In an embodiment a clustered network caninclude data storage systems (e.g., 102, 104) residing in a samegeographic location (e.g., in a single onsite rack of data storagedevices).

In the illustrated example, one or more host devices 108, 110 which maycomprise, for example, client devices, personal computers (PCs),computing devices used for storage (e.g., storage servers), and othercomputers or peripheral devices (e.g., printers), are coupled to therespective data storage systems 102, 104 by storage network connections112, 114. Network connection may comprise a local area network (LAN) orwide area network (WAN), for example, that utilizes Network AttachedStorage (NAS) protocols, such as a Common Internet File System (CIFS)protocol or a Network File System (NFS) protocol to exchange datapackets, a Storage Area Network (SAN) protocol, such as Small ComputerSystem Interface (SCSI) or Fiber Channel Protocol (FCP), an objectprotocol, such as S3, etc. Illustratively, the host devices 108, 110 maybe general-purpose computers running applications, and may interact withthe data storage systems 102, 104 using a client/server model forexchange of information. That is, the host device may request data fromthe data storage system (e.g., data on a storage device managed by anetwork storage control configured to process I/O commands issued by thehost device for the storage device), and the data storage system mayreturn results of the request to the host device via one or more storagenetwork connections 112, 114.

The nodes 116, 118 on clustered data storage systems 102, 104 cancomprise network or host nodes that are interconnected as a cluster toprovide data storage and management services, such as to an enterprisehaving remote locations, cloud storage (e.g., a storage endpoint may bestored within a data cloud), etc., for example. Such a node in theclustered network environment 100 can be a device attached to thenetwork as a connection point, redistribution point or communicationendpoint, for example. A node may be capable of sending, receiving,and/or forwarding information over a network communications channel, andcould comprise any device that meets any or all of these criteria. Oneexample of a node may be a data storage and management server attachedto a network, where the server can comprise a general purpose computeror a computing device particularly configured to operate as a server ina data storage and management system.

In an example, a first cluster of nodes such as the nodes 116, 118(e.g., a first set of storage controllers configured to provide accessto a first storage aggregate comprising a first logical grouping of oneor more storage devices) may be located on a first storage site. Asecond cluster of nodes, not illustrated, may be located at a secondstorage site (e.g., a second set of storage controllers configured toprovide access to a second storage aggregate comprising a second logicalgrouping of one or more storage devices). The first cluster of nodes andthe second cluster of nodes may be configured according to a disasterrecovery configuration where a surviving cluster of nodes providesswitchover access to storage devices of a disaster cluster of nodes inthe event a disaster occurs at a disaster storage site comprising thedisaster cluster of nodes (e.g., the first cluster of nodes providesclient devices with switchover data access to storage devices of thesecond storage aggregate in the event a disaster occurs at the secondstorage site).

As illustrated in the clustered network environment 100, nodes 116, 118can comprise various functional components that coordinate to providedistributed storage architecture for the cluster. For example, the nodescan comprise network modules 120, 122 and disk modules 124, 126. Networkmodules 120, 122 can be configured to allow the nodes 116, 118 (e.g.,network storage controllers) to connect with host devices 108, 110 overthe storage network connections 112, 114, for example, allowing the hostdevices 108, 110 to access data stored in the distributed storagesystem. Further, the network modules 120, 122 can provide connectionswith one or more other components through the cluster fabric 106. Forexample, in FIG. 1, the network module 120 of node 116 can access asecond data storage device by sending a request through the disk module126 of node 118.

Disk modules 124, 126 can be configured to connect one or more datastorage devices 128, 130, such as disks or arrays of disks, flashmemory, or some other form of data storage, to the nodes 116, 118. Thenodes 116, 118 can be interconnected by the cluster fabric 106, forexample, allowing respective nodes in the cluster to access data on datastorage devices 128, 130 connected to different nodes in the cluster.Often, disk modules 124, 126 communicate with the data storage devices128, 130 according to the SAN protocol, such as SCSI or FCP, forexample. Thus, as seen from an operating system on nodes 116, 118, thedata storage devices 128, 130 can appear as locally attached to theoperating system. In this manner, different nodes 116, 118, etc. mayaccess data blocks through the operating system, rather than expresslyrequesting abstract files.

It should be appreciated that, while the clustered network environment100 illustrates an equal number of network and disk modules, otherembodiments may comprise a differing number of these modules. Forexample, there may be a plurality of network and disk modulesinterconnected in a cluster that does not have a one-to-onecorrespondence between the network and disk modules. That is, differentnodes can have a different number of network and disk modules, and thesame node can have a different number of network modules than diskmodules.

Further, a host device 108, 110 can be networked with the nodes 116, 118in the cluster, over the storage networking connections 112, 114. As anexample, respective host devices 108, 110 that are networked to acluster may request services (e.g., exchanging of information in theform of data packets) of nodes 116, 118 in the cluster, and the nodes116, 118 can return results of the requested services to the hostdevices 108, 110. In an embodiment, the host devices 108, 110 canexchange information with the network modules 120, 122 residing in thenodes 116, 118 (e.g., network hosts) in the data storage systems 102,104.

In an embodiment, the data storage devices 128, 130 comprise volumes132, which is an implementation of storage of information onto diskdrives or disk arrays or other storage (e.g., flash) as a file-systemfor data, for example. In an example, a disk array can include alltraditional hard drives, all flash drives, or a combination oftraditional hard drives and flash drives. Volumes can span a portion ofa disk, a collection of disks, or portions of disks, for example, andtypically define an overall logical arrangement of file storage on diskspace in the storage system. In an embodiment a volume can comprisestored data as one or more files that reside in a hierarchical directorystructure within the volume.

Volumes are typically configured in formats that may be associated withparticular storage systems, and respective volume formats typicallycomprise features that provide functionality to the volumes, such asproviding an ability for volumes to form clusters. For example, where afirst storage system may utilize a first format for their volumes, asecond storage system may utilize a second format for their volumes.

In the clustered network environment 100, the host devices 108, 110 canutilize the data storage systems 102, 104 to store and retrieve datafrom the volumes 132. In this embodiment, for example, the host device108 can send data packets to the network module 120 in the node 116within data storage system 102. The node 116 can forward the data to thedata storage device 128 using the disk module 124, where the datastorage device 128 comprises volume 132A. In this way, in this example,the host device can access the volume 132A, to store and/or retrievedata, using the data storage system 102 connected by the storage networkconnection 112. Further, in this embodiment, the host device 110 canexchange data with the network module 122 in the node 118 within thedata storage system 104 (e.g., which may be remote from the data storagesystem 102). The node 118 can forward the data to the data storagedevice 130 using the disk module 126, thereby accessing volume 1328associated with the data storage device 130.

It may be appreciated that offloading the management of sensor data andthe generation of custom views of sensor data may be implemented withinthe clustered network environment 100. It may be appreciated thatoffloading the management of sensor data and the generation of customviews of sensor data may be implemented for and/or between any type ofcomputing environment, and may be transferrable between physical devices(e.g., node 116, node 118, a desktop computer, a tablet, a laptop, awearable device, a mobile device, a storage device, a server, etc.)and/or a cloud computing environment (e.g., remote to the clusterednetwork environment 100).

FIG. 2 is an illustrative example of a data storage system 200 (e.g.,102, 104 in FIG. 1), providing further detail of an embodiment ofcomponents that may implement one or more of the techniques and/orsystems described herein. The data storage system 200 comprises a node202 (e.g., nodes 116, 118 in FIG. 1), and a data storage device 234(e.g., data storage devices 128, 130 in FIG. 1). The node 202 may be ageneral purpose computer, for example, or some other computing deviceparticularly configured to operate as a storage server. A host device205 (e.g., 108, 110 in FIG. 1) can be connected to the node 202 over anetwork 216, for example, to provide access to files and/or other datastored on the data storage device 234. In an example, the node 202comprises a storage controller that provides client devices, such as thehost device 205, with access to data stored within data storage device234.

The data storage device 234 can comprise mass storage devices, such asdisks 224, 226, 228 of a disk array 218, 220, 222. It will beappreciated that the techniques and systems, described herein, are notlimited by the example embodiment. For example, disks 224, 226, 228 maycomprise any type of mass storage devices, including but not limited tomagnetic disk drives, flash memory, and any other similar media adaptedto store information, including, for example, data (D) and/or parity (P)information.

The node 202 comprises one or more processors 204, a memory 206, anetwork adapter 210, a cluster access adapter 212, and a storage adapter214 interconnected by a system bus 242. The data storage system 200 alsoincludes an operating system 208 installed in the memory 206 of the node202 that can, for example, implement a Redundant Array of Independent(or Inexpensive) Disks (RAID) optimization technique to optimize areconstruction process of data of a failed disk in an array.

The operating system 208 can also manage communications for the datastorage system, and communications between other data storage systemsthat may be in a clustered network, such as attached to a cluster fabric215 (e.g., 106 in FIG. 1). Thus, the node 202, such as a network storagecontroller, can respond to host device requests to manage data on thedata storage device 234 (e.g., or additional clustered devices) inaccordance with these host device requests. The operating system 208 canoften establish one or more file systems on the data storage system 200,where a file system can include software code and data structures thatimplement a persistent hierarchical namespace of files and directories,for example. As an example, when a new data storage device (not shown)is added to a clustered network system, the operating system 208 isinformed where, in an existing directory tree, new files associated withthe new data storage device are to be stored. This is often referred toas “mounting” a file system.

In the example data storage system 200, memory 206 can include storagelocations that are addressable by the processors 204 and adapters 210,212, 214 for storing related software application code and datastructures. The processors 204 and adapters 210, 212, 214 may, forexample, include processing elements and/or logic circuitry configuredto execute the software code and manipulate the data structures. Theoperating system 208, portions of which are typically resident in thememory 206 and executed by the processing elements, functionallyorganizes the storage system by, among other things, invoking storageoperations in support of a file service implemented by the storagesystem. It will be apparent to those skilled in the art that otherprocessing and memory mechanisms, including various computer readablemedia, may be used for storing and/or executing application instructionspertaining to the techniques described herein. For example, theoperating system can also utilize one or more control files (not shown)to aid in the provisioning of virtual machines.

The network adapter 210 includes the mechanical, electrical andsignaling circuitry needed to connect the data storage system 200 to ahost device 205 over a network 216, which may comprise, among otherthings, a point-to-point connection or a shared medium, such as a localarea network. The host device 205 (e.g., 108, 110 of FIG. 1) may be ageneral-purpose computer configured to execute applications. Asdescribed above, the host device 205 may interact with the data storagesystem 200 in accordance with a client/host model of informationdelivery.

The storage adapter 214 cooperates with the operating system 208executing on the node 202 to access information requested by the hostdevice 205 (e.g., access data on a storage device managed by a networkstorage controller). The information may be stored on any type ofattached array of writeable media such as magnetic disk drives, flashmemory, and/or any other similar media adapted to store information. Inthe example data storage system 200, the information can be stored indata blocks on the disks 224, 226, 228. The storage adapter 214 caninclude input/output (I/O) interface circuitry that couples to the disksover an I/O interconnect arrangement, such as a storage area network(SAN) protocol (e.g., Small Computer System Interface (SCSI), iSCSI,hyperSCSI, Fiber Channel Protocol (FCP)). The information is retrievedby the storage adapter 214 and, if necessary, processed by the one ormore processors 204 (or the storage adapter 214 itself) prior to beingforwarded over the system bus 242 to the network adapter 210 (and/or thecluster access adapter 212 if sending to another node in the cluster)where the information is formatted into a data packet and returned tothe host device 205 over the network 216 (and/or returned to anothernode attached to the cluster over the cluster fabric 215).

In an embodiment, storage of information on disk arrays 218, 220, 222can be implemented as one or more storage volumes 230, 232 that arecomprised of a cluster of disks 224, 226, 228 defining an overalllogical arrangement of disk space. The disks 224, 226, 228 that compriseone or more volumes are typically organized as one or more groups ofRAIDs. As an example, volume 230 comprises an aggregate of disk arrays218 and 220, which comprise the cluster of disks 224 and 226.

In an embodiment, to facilitate access to disks 224, 226, 228, theoperating system 208 may implement a file system (e.g., write anywherefile system) that logically organizes the information as a hierarchicalstructure of directories and files on the disks. In this embodiment,respective files may be implemented as a set of disk blocks configuredto store information, whereas directories may be implemented asspecially formatted files in which information about other files anddirectories are stored.

Whatever the underlying physical configuration within this data storagesystem 200, data can be stored as files within physical and/or virtualvolumes, which can be associated with respective volume identifiers,such as file system identifiers (FSIDs), which can be 32-bits in lengthin one example.

A physical volume corresponds to at least a portion of physical storagedevices whose address, addressable space, location, etc. doesn't change,such as at least some of one or more data storage devices 234 (e.g., aRedundant Array of Independent (or Inexpensive) Disks (RAID system)).Typically the location of the physical volume doesn't change in that the(range of) address(es) used to access it generally remains constant.

A virtual volume, in contrast, is stored over an aggregate of disparateportions of different physical storage devices. The virtual volume maybe a collection of different available portions of different physicalstorage device locations, such as some available space from each of thedisks 224, 226, and/or 228. It will be appreciated that since a virtualvolume is not “tied” to any one particular storage device, a virtualvolume can be said to include a layer of abstraction or virtualization,which allows it to be resized and/or flexible in some regards.

Further, a virtual volume can include one or more logical unit numbers(LUNs) 238, directories 236, Qtrees 235, and files 240. Among otherthings, these features, but more particularly LUNS, allow the disparatememory locations within which data is stored to be identified, forexample, and grouped as data storage unit. As such, the LUNs 238 may becharacterized as constituting a virtual disk or drive upon which datawithin the virtual volume is stored within the aggregate. For example,LUNs are often referred to as virtual drives, such that they emulate ahard drive from a general purpose computer, while they actually comprisedata blocks stored in various parts of a volume.

In an embodiment, one or more data storage devices 234 can have one ormore physical ports, wherein each physical port can be assigned a targetaddress (e.g., SCSI target address). To represent respective volumesstored on a data storage device, a target address on the data storagedevice can be used to identify one or more LUNs 238. Thus, for example,when the node 202 connects to a volume 230, 232 through the storageadapter 214, a connection between the node 202 and the one or more LUNs238 underlying the volume is created.

In an embodiment, respective target addresses can identify multipleLUNs, such that a target address can represent multiple volumes. The I/Ointerface, which can be implemented as circuitry and/or software in thestorage adapter 214 or as executable code residing in memory 206 andexecuted by the processors 204, for example, can connect to volume 230by using one or more addresses that identify the one or more LUNs 238.

It may be appreciated that offloading the management of sensor data andthe generation of custom views of sensor data may be implemented for thedata storage system 200. It may be appreciated that offloading themanagement of sensor data and the generation of custom views of sensordata may be implemented for and/or between any type of computingenvironment, and may be transferrable between physical devices (e.g.,node 202, host device 205, a desktop computer, a tablet, a laptop, awearable device, a mobile device, a storage device, a server, etc.)and/or a cloud computing environment (e.g., remote to the node 202and/or the host device 205).

One embodiment of offloading the management of sensor data isillustrated by an exemplary method 300 of FIG. 3 and one embodiment ofgenerating of custom views of sensor data is illustrated by an exemplarymethod 400 of FIG. 4, which are further described in conjunction withsystem 500 of FIG. 5. A data network 514 (e.g., an Internet of Thingsnetwork, an Industrial Internet of Things network, or any other type ofnetwork) may be associated with an edge network 516. Various sensors andsensor gateways 522 may be connected to the edge network 516. Forexample, security cameras, vehicles, sensors attached to industrialequipment, location beacons, and/or a wide variety of sensors or devicescoupled to sensors may be connected to the edge network 516. Anapplication 524 within the edge network 516 may be configured toreceive/collect sensor data from the sensors or sensor gateways 522. Theapplication 524 may be configured to communicate with other applicationswithin the data network 514 using a message-based communication softwarestack connected to the data network 514 (e.g., an IoT or IIoT stack).For example, the application 524 may be capable of communicating with anapplication 526 hosted within a core network 518 of the data network514. The application 526 may be configured to, among other things, runanalytics or perform other processing upon the sensor data. The corenetwork 518 may comprise dedicated hardware and/or software used toexecute the application 526. The application 524 and/or the application526 may be configured to communicate with an application 528 hostedwithin a cloud network 520 associated with the data network 514. Theapplication 528 may be configured to, among other things, perform longterm processing/analytics of the sensor data and/or reconfigure thesensors or sensor gateways 522.

Large sensor data (e.g., images, video, spectrum information, etc.)and/or high frequency sensor data (e.g., a sensor generating millions ofdata samples a second) can overwhelm the message-based communicationsoftware stack used to communicate over the data network 514. This cancause increased latency, reliability issues, inability to keep up withincoming messages, and other performance problems with the message-basedcommunication software stack used to communicate over the data network514.

Accordingly, as provided herein, a storage offload engine component 530is configured to reroute the sensor data ordinarily contained withinmessages being routed through the data network 514 to instead beingrouted to a separate offloaded storage environment 502. The offloadedstorage environment 502 may comprise computing devices, such as a firstcomputing device 504 associated with the edge network 516, a secondcomputing device 506 associated with the core network 518, a thirdcomputing device 508 associated with the cloud network 520, that executestorage operating systems and storage services capable of moreefficiently managing the storage, processing, and transmission of sensordata than the message-based communication software stack of the datanetwork 514. Offloading these tasks from the message-based communicationsoftware stack of the data network 514 to the offloaded storageenvironment 502 dedicated to managing sensor data will reduce the burdenupon the data network 514, thus improving latency, scalability, andoperation of devices within the data network 514.

In an example, sensor data is received from a sensor or sensor gatewayof the edge network 516 within the data network 514. The storage offloadengine component 530 embedded in, integrated in, or otherwise coupledwith the application 524 may evaluate the sensor data to determinewhether the sensor data should be routed through the data network 514using the message-based communication software stack of the data network514 or should be routed to the offloaded storage environment 502 thusskipping the message-based communication software stack. In an example,the storage offload engine component 530 may route the sensor data tothe first computing device 504 of the offloaded storage environment 502based upon the sensor data having a size greater than a size threshold.Such large sensor data may otherwise encumber the message-basedcommunication software stack and data network 514, but the firstcomputing device 504 may be tailored to efficiently store, process, andtransmit large sensor data. In another example, the storage offloadengine component 530 may route the sensor data through a message 532 tothe first computing device 504 of the offloaded storage environment 502based upon the sensor data being generated at a frequency greater than afrequency threshold. High frequency sensor data may otherwise encumberthe message-based communication software stack and data network 514, butthe first computing device 504 may be tailored to efficiently store,process, and transmit high frequency sensor data. A variety of othercriteria may be used to determine whether the stack of the data network514 or the offloaded storage environment 502 should receive and managesensor data. Such criteria may correspond to a data type of sensor data,a location that generated the sensor data, a particular sensor or typeof sensor that generated the sensor data, a flag/indicator associatedwith the sensor data, the sensitivity of the sensor data, thevulnerability of the sensor data to alteration or unauthorized access,the importance of the sensor data, the ultimate destination of thesensor data, the need for additional processing of the sensor data,and/or any other property of the sensor data or sensor that generatedthe sensor data.

At 302, the message 532 containing the sensor data generated by thesensor or sensor gateway of the edge network 516 of the data network 514and rerouted by the storage offload engine component 530 is received bythe first computing device 504 of the offloaded storage environment 502.The storage offload engine component 530 routed the message 532 to thefirst computing device 504 based upon a property of the sensor data(e.g., a size property, a frequency property, a type of sensorgenerating the sensor data, a data format of the sensor data, a creationtime of the sensor data, a flag or other indicator, etc.) indicatingthat the sensor data is to be offloaded from the stack of the datanetwork 514 to the first computing device 504.

At 304, the sensor data is stored within storage managed by the firstcomputing device 504. In an example, the first computing device 504 maymanage locally attached storage and/or remotely accessible storage. Forexample, the first computing device 504 may be in communication with theedge network 516 and may store the sensor data within storage associatedwith the edge network 516 and/or with the first computing device 504.Thus, the sensor data may be initially stored within storage accessiblethrough the edge network 516 and/or the first computing device 504. Aswill be described later in further detail, the sensor data may betransferred 510 to the second computing device 506 in communication withthe core network 518, and thus the sensor data may be accessible throughthe core network 518 and/or the second computing device 506. Similar,the sensor data may be transferred 512 to the third computing device 508in communication with the cloud network 520, and thus the sensor datamay be accessible through the cloud network 520 and/or the thirdcomputing device 508.

In an example, the first computing device 504 may store a set of datasamples as a single object (e.g., a file or other data structure). Forexample, instead of storing a 100,000 data samples collected over 1second as individual objects, the 100,000 data samples are stored as asingle object of 1 second of data samples. In an example, the firstcomputing device 504 may deduplicate the sensor data based upon adeduplication policy (e.g., a fingerprint of sensor data may be comparedwith fingerprints of already stored data to determine whether the sensordata is unique and should be stored or is a duplicate of already storeddata and merely a pointer should be stored to point to the alreadystored data). The sensor data may be inline deduplicated before thesensor data is stored within the storage or may be backgrounddeduplicated after being stored to the storage. In an example, the firstcomputing device 504 may compress the sensor data based upon acompression policy. In an example, the first computing device 504 mayencrypt the sensor data based upon an encryption policy. In an example,the sensor data is stored within an object that is named based uponvarious factors, such as a creation date, a sensor that generated thesensor data, a data type of the sensor data, etc. In an example, thesensor data is stored with ancillary metadata associated with the objectthat is extracted, derived and/or associated from/with the sensor data,such as when the sensor data was created, where the sensor data isstored, what sensor or sensor gateway created the sensor data, alocation of where the sensor data was generated, a data type orinformation comprised within the sensor data, attestation andtraceability information (timestamp information, event information,information relating to processing and analytics performed upon thesensor data by a particular application at a particular point in time,locations and times at which the sensor data has been stored,transmitted, or processed, etc.), validation and verificationinformation (e.g., a checksum, a hash, a fingerprint), and/or otherproperties of the sensor or sensor gateway, sensor data, and/or othervarious factors. In this way, various storage functionality may beimplemented within the offloaded storage environment 502 for theoffloaded sensor data.

At 306, a handle is generated to identify the sensor data. The handlemay be a unique identifier used to reference and/or locate the sensordata. Various information may be encoded into the handle, such asidentification information assigned by the application 524,identification information assigned by the first computing device 504,when the sensor data was created, where the sensor data is stored, whatsensor created the sensor data, a location of where the sensor data wasgenerated, a data type or information comprised within the sensor data,attestation and traceability information (timestamp information, eventinformation, information relating to processing and analytics performedupon the sensor data by a particular application at a particular pointin time, locations and times at which the sensor data has been stored,transmitted, or processed, etc.), validation and verificationinformation (e.g., a checksum, a hash, a fingerprint), and/or otherproperties of the sensor and/or sensor data. The structure and contentsof the handle may be opaque to the applications 524, 526, 528, or thestructure and contents of the handle may be visible to the applications524, 526, 528. As will be described later in further detail, the handlemay be used to verify the integrity of sensor data (e.g., a device usingthe handle to access the sensor data can use the handle to ensure thatthe received sensor data is in fact the requested sensor data), createcustom views of particular sensor data (e.g., view sensor data generatedby particular types of sensors during a particular timeframe), etc.

At 308, the sensor data is replaced within the message 532 with thehandle. At 310, the message, with the handle (e.g., and without thesensor data), is transmitted to a device within the data network 514.For example, an application executing on the device may be registered orotherwise associated with the sensor data or the sensor (e.g., asecurity application that uses image recognition functionality to detectthreats within images captured by a camera sensor). The device may beconnected to the edge network 516, the core network 518 (e.g., adedicated server executing the security application), and/or the cloudnetwork 520 (e.g., a virtual machine within the cloud network 520 thatexecutes the security application).

At 312, access by the device to the sensor data using the handle may befacilitated. For example, the application 526 within the core network518 (or the application 528 within the cloud network 520) may transmit arequest to the offloaded storage environment 502 to access the sensordata. The request may comprise the handle. The second computing device506 connected to the core network 518 may receive the request. Thesecond computing device 506 may evaluate the handle to determine whetherthe sensor data is locally available to the second computing device 506(e.g., within local storage) or remotely available (e.g., within storagemanaged by the first computing device 504). Accordingly, the secondcomputing device 506 uses the handle to facilitate data access to thesensor data (e.g., execute a write operation upon the sensor data;retrieve and transmit the sensor data to the second computing device 506that has requested to read the sensor data; prefetching and datareplication and placement rules based on access patterns; etc.). In anexample of facilitating access to the sensor data, the sensor data maybe represented or accessible through a file system or other hierarchicalstructure to devices within the data network 514.

The sensor data may be transferred 510, 512 within the offloaded storageenvironment 502 to storage and/or computing devices, within theoffloaded storage environment 502, that are in communication with theedge network 516, the core network 518, and/or the cloud network 520based upon various policies, on-demand when requested, etc. For example,sensor data may be replicated or mirrored from the storage managed bythe first computing device 504 to second storage managed by the secondcomputing device 506 or the third computing device 508 based upon areplication policy (e.g., specifying what sensor data should bereplicated, when to replicate the sensor data, where to replicate thesensor data, etc.) or mirroring policy, and thus multiple copies of thesensor data may be stored within the offloaded storage environment 502.

In an example, the sensor may be migrated from the storage of the firstcomputing device 504 to other storage managed by other computing devicesbased upon a migration policy. For example, the migration policy mayspecify that sensor data should be migrated to storage of the thirdcomputing device 508 based upon a majority or threshold number ofrequests for that sensor data being received from the cloud network 520because the third computing device 508 is in communication with thecloud network 520. Similarly, the migration policy may specify thatsensor data should be migrated to storage of the second computing device506 based upon a majority or threshold number of requests for thatsensor data being received from the core network 518 because the secondcomputing device 506 is in communication with the core network 518. Themigration policy may specify that infrequently accessed sensor datashould be stored within particular storage of the offloaded storageenvironment 502 and that more frequently accessed sensor data should bestored in different storage. In an example, sensor data is transmittedwithin the offloaded storage environment 502 on demand, such as wherethe second computing device 506 retrieves the sensor data from storageof the first computing device 504 in order to process the request forthe sensor data from the application 526 within the data network 514.

In an example, the computing devices of the offloaded storageenvironment 502 may process sensor data based upon events or policies.For example, a computing device may perform analytics upon sensor data,track statistics of sensor data (e.g., the amount and type of sensordata being stored, when and how frequently sensor data is accessed, thelocation of sensor data, what sensors are producing sensor data and howmuch, etc.), and/or other functionality that may be offloaded from thedata network 514 to the offloaded storage environment 502 (e.g.,performing image recognition to detect threats, evaluating location datato route a vehicle, evaluating industrial health sensor data to generatean alert of equipment failing, etc.). Various events or polices maytrigger execution of such analytics or functionality (e.g., when athreshold amount of a certain type of sensor data is received, when athreshold amount of data from a particular sensor is received, an amountof time elapsing, etc.).

In an example, the computing devices of the offloaded storageenvironment 502 may transform sensor data based upon events or policies.For example, a computing device may convert between data formats, createderived data representations (e.g. frequency transforms, imagethumbnails, multiple resolution video encodings, region selection,region masking, downsampling, decimation, etc.), and/or otherfunctionality that may be offloaded from the data network 514 to theoffloaded storage environment 502. Various events or polices may triggerexecution of such transformations (e.g., when a threshold amount of acertain type of sensor data is received, when a threshold amount of datafrom a particular sensor is received, an amount of time elapsing, etc.).

Devices within the data network 514 can use metadata associated withsensor data stored in the offloaded storage environment 502 to providecustom views of sensor data. At 402, a device within the data network514 (e.g., a device within the cloud network 520 executing theapplication 528 for analyzing sensor data) receives messages fromcomputing devices within the offloaded storage environment 502. Themessages may be associated with a plurality of sensor data generated bythe sensors or sensor gateways 522 of the edge network 516. The sensordata may be stored within storage managed by the offloaded storageenvironment 502, such as storage associated with the first computingdevice 504 in communication with the edge network 516, a secondcomputing device 506 in communication with the core network 518, and/ora third computing device 508 in communication with the cloud network520.

At 404, the device may access metadata to obtain information relating tothe plurality of sensor data. For example, metadata for sensor data mayinclude various information, such as when the sensor data was created,where the sensor data is stored, what sensor created the sensor data, alocation of where the sensor data was generated, a data type orinformation comprised within the sensor data, attestation andtraceability information, validation and verification information,and/or other properties of the sensor and/or sensor data. Suchinformation may be requested from the computing devices in the offloadedstorage environment 502.

At 406, a request may be constructed for a view of a subset of theplurality of sensor data. The request may comprise metadata conditionsof the subset of the plurality of sensor data. In an example, therequest may specify metadata conditions for sensor data collected from aparticular sensor or set of sensors (e.g., all photos captured within aparticular distance proximity). In an example, the request may specifymetadata for sensor data having a first data type (e.g., all temperaturesensor data). In an example, the request may specify handles for sensordata having a certain date within a specified timespan. The request mayalso specify metadata indicating how the resulting matching sensor datais to be structurally organized when presented to the device. In anexample, the request may specify metadata for sensor data residing in aparticular data repository (e.g., a particular volume, storage device,database, database table, etc.). It may be appreciated that the requestmay specify various parameters and/or combinations thereof used torequest certain sensor data (e.g., request video sensor data that hasbeen processed by a particular application and stored within aparticular database).

At 408, the request may be transmitted to the offloaded storageenvironment 502 such as from the application 528 to the third computingdevice 508. The third computing device 508 may use the metadata withinthe request to identify, locate, and retrieve the requested sensor datafrom storage within the offloaded storage environment 502. At 410, thedevice receives the view of the sensor data from the offloaded storageenvironment 502, such as where the application 528 receives the viewfrom the third computing device 508. The view is a custom tailored viewof particular sensor data maintained within the offloaded storageenvironment 502 on behalf of the data network 514. In an example, thedevice may obtain access to sensor data, using the view, through a filesystem or other hierarchical, list, or graph structure hosted by theoffloaded storage environment 502 and exposed to device within the datanetwork 514. The view may be processed by analytics or otherfunctionality of the device. The view may be displayed through a userinterface.

In an example, the view of sensor data comprises first sensor datahaving a first handle bound to the first sensor data (e.g., the firsthandle is encoded based upon a property of the first sensor data such aswith checksum, security information, a hash or fingerprint, etc.). Thedevice may use the information encoded into the first handle to validatethe first sensor data as being the sensor data requested by the device.For example, the device may use encoded checksum information to validatethe first sensor data. In an example, the device may evaluate timestampsof the first sensor data to identify events associated with the firstsensor data (e.g., a creation timestamp, a storage timestamp, a modifytimestamp, a replication timestamp, a migration timestamp, a timestampindicating certain analysis was performed upon the first sensor data,etc.).

Still another embodiment involves a computer-readable medium 600comprising processor-executable instructions configured to implement oneor more of the techniques presented herein. An example embodiment of acomputer-readable medium or a computer-readable device that is devisedin these ways is illustrated in FIG. 6, wherein the implementationcomprises a computer-readable medium 608, such as a compactdisc-recordable (CD-R), a digital versatile disc-recordable (DVD-R),flash drive, a platter of a hard disk drive, etc., on which is encodedcomputer-readable data 606. This computer-readable data 606, such asbinary data comprising at least one of a zero or a one, in turncomprises a processor-executable computer instructions 604 configured tooperate according to one or more of the principles set forth herein. Insome embodiments, the processor-executable computer instructions 604 areconfigured to perform a method 602, such as at least some of theexemplary method 300 of FIG. 3 and/or at least some of the exemplarymethod 400 of FIG. 4, for example. In some embodiments, theprocessor-executable computer instructions 604 are configured toimplement a system, such as at least some of the exemplary system 500 ofFIG. 5, for example. Many such computer-readable media are contemplatedto operate in accordance with the techniques presented herein.

FIG. 7 is a diagram illustrating an example operating environment 700 inwhich an embodiment of the techniques described herein may beimplemented. In one example, the techniques described herein may beimplemented within a client device 728, such as a laptop, tablet,personal computer, mobile device, wearable device, etc. In anotherexample, the techniques described herein may be implemented within astorage controller 730, such as a node configured to manage the storageand access to data on behalf of the client device 728 and/or otherclient devices. In another example, the techniques described herein maybe implemented within a distributed computing platform 702 such as acloud computing environment (e.g., a cloud storage environment, amulti-tenant platform, etc.) configured to manage the storage and accessto data on behalf of the client device 728 and/or other client devices.

In yet another example, at least some of the techniques described hereinare implemented across one or more of the client device 728, the storagecontroller 730, and the distributed computing platform 702. For example,the client device 728 may transmit operations, such as data operationsto read data and write data and metadata operations (e.g., a create fileoperation, a rename directory operation, a resize operation, a setattribute operation, etc.), over a network 726 to the storage controller730 for implementation by the storage controller 730 upon storage. Thestorage controller 730 may store data associated with the operationswithin volumes or other datan objects/structures hosted within locallyattached storage, remote storage hosted by other computing devicesaccessible over the network 726, storage provided by the distributedcomputing platform 702, etc. The storage controller 730 may replicatethe data and/or the operations to other computing devices so that one ormore replicas, such as a destination storage volume that is maintainedas a replica of a source storage volume, are maintained. Such replicascan be used for disaster recovery and failover.

The storage controller 730 may store the data or a portion thereofwithin storage hosted by the distributed computing platform 702 bytransmitting the data to the distributed computing platform 702. In oneexample, the storage controller 730 may locally store frequentlyaccessed data within locally attached storage. Less frequently accesseddata may be transmitted to the distributed computing platform 702 forstorage within a data storage tier 708. The data storage tier 708 maystore data within a service data store 720, and may store clientspecific data within client data stores assigned to such clients such asa client (1) data store 722 used to store data of a client (1) and aclient (N) data store 724 used to store data of a client (N). The datastores may be physical storage devices or may be defined as logicalstorage, such as a virtual volume, LUNs, or other logical organizationsof data that can be defined across one or more physical storage devices.In another example, the storage controller 730 transmits and stores allclient data to the distributed computing platform 702. In yet anotherexample, the client device 728 transmits and stores the data directly tothe distributed computing platform 702 without the use of the storagecontroller 730.

The management of storage and access to data can be performed by one ormore storage virtual machines (SMVs) or other storage applications thatprovide software as a service (SaaS) such as storage software services.In one example, an SVM may be hosted within the client device 728,within the storage controller 730, or within the distributed computingplatform 702 such as by the application server tier 706. In anotherexample, one or more SVMs may be hosted across one or more of the clientdevice 728, the storage controller 730, and the distributed computingplatform 702.

In one example of the distributed computing platform 702, one or moreSVMs may be hosted by the application server tier 706. For example, aserver (1) 716 is configured to host SVMs used to execute applicationssuch as storage applications that manage the storage of data of theclient (1) within the client (1) data store 722. Thus, an SVM executingon the server (1) 716 may receive data and/or operations from the clientdevice 728 and/or the storage controller 730 over the network 726. TheSVM executes a storage application to process the operations and/orstore the data within the client (1) data store 722. The SVM maytransmit a response back to the client device 728 and/or the storagecontroller 730 over the network 726, such as a success message or anerror message. In this way, the application server tier 706 may hostSVMs, services, and/or other storage applications using the server (1)716, the server (N) 718, etc.

A user interface tier 704 of the distributed computing platform 702 mayprovide the client device 728 and/or the storage controller 730 withaccess to user interfaces associated with the storage and access of dataand/or other services provided by the distributed computing platform702. In an example, a service user interface 710 may be accessible fromthe distributed computing platform 702 for accessing services subscribedto by clients and/or storage controllers, such as data replicationservices, application hosting services, data security services, humanresource services, warehouse tracking services, accounting services,etc. For example, client user interfaces may be provided tocorresponding clients, such as a client (1) user interface 712, a client(N) user interface 714, etc. The client (1) can access various servicesand resources subscribed to by the client (1) through the client (1)user interface 712, such as access to a web service, a developmentenvironment, a human resource application, a warehouse trackingapplication, and/or other services and resources provided by theapplication server tier 706, which may use data stored within the datastorage tier 708.

The client device 728 and/or the storage controller 730 may subscribe tocertain types and amounts of services and resources provided by thedistributed computing platform 702. For example, the client device 728may establish a subscription to have access to three virtual machines, acertain amount of storage, a certain type/amount of data redundancy, acertain type/amount of data security, certain service level agreements(SLAs) and service level objectives (SLOs), latency guarantees,bandwidth guarantees, access to execute or host certain applications,etc. Similarly, the storage controller 730 can establish a subscriptionto have access to certain services and resources of the distributedcomputing platform 702.

As shown, a variety of clients, such as the client device 728 and thestorage controller 730, incorporating and/or incorporated into a varietyof computing devices may communicate with the distributed computingplatform 702 through one or more networks, such as the network 726. Forexample, a client may incorporate and/or be incorporated into a clientapplication (e.g., software) implemented at least in part by one or moreof the computing devices.

Examples of suitable computing devices include personal computers,server computers, desktop computers, nodes, storage servers, storagecontrollers, laptop computers, notebook computers, tablet computers orpersonal digital assistants (PDAs), smart phones, cell phones, andconsumer electronic devices incorporating one or more computing devicecomponents, such as one or more electronic processors, microprocessors,central processing units (CPU), or controllers. Examples of suitablenetworks include networks utilizing wired and/or wireless communicationtechnologies and networks operating in accordance with any suitablenetworking and/or communication protocol (e.g., the Internet). In usecases involving the delivery of customer support services, the computingdevices noted represent the endpoint of the customer support deliveryprocess, i.e., the consumer's device.

The distributed computing platform 702, such as a multi-tenant businessdata processing platform or cloud computing environment, may includemultiple processing tiers, including the user interface tier 704, theapplication server tier 706, and a data storage tier 708. The userinterface tier 704 may maintain multiple user interfaces, includinggraphical user interfaces and/or web-based interfaces. The userinterfaces may include the service user interface 710 for a service toprovide access to applications and data for a client (e.g., a “tenant”)of the service, as well as one or more user interfaces that have beenspecialized/customized in accordance with user specific requirements,which may be accessed via one or more APIs.

The service user interface 710 may include components enabling a tenantto administer the tenant's participation in the functions andcapabilities provided by the distributed computing platform 702, such asaccessing data, causing execution of specific data processingoperations, etc. Each processing tier may be implemented with a set ofcomputers, virtualized computing environments such as a storage virtualmachine or storage virtual server, and/or computer components includingcomputer servers and processors, and may perform various functions,methods, processes, or operations as determined by the execution of asoftware application or set of instructions.

The data storage tier 708 may include one or more data stores, which mayinclude the service data store 720 and one or more client data stores.Each client data store may contain tenant-specific data that is used aspart of providing a range of tenant-specific business and storageservices or functions, including but not limited to ERP, CRM, eCommerce,Human Resources management, payroll, storage services, etc. Data storesmay be implemented with any suitable data storage technology, includingstructured query language (SQL) based relational database managementsystems (RDBMS), file systems hosted by operating systems, objectstorage, etc.

In accordance with one embodiment of the invention, the distributedcomputing platform 702 may be a multi-tenant and service platformoperated by an entity in order to provide multiple tenants with a set ofbusiness related applications, data storage, and functionality. Theseapplications and functionality may include ones that a business uses tomanage various aspects of its operations. For example, the applicationsand functionality may include providing web-based access to businessinformation systems, thereby allowing a user with a browser and anInternet or intranet connection to view, enter, process, or modifycertain types of business information or any other type of information.

In an embodiment, the described methods and/or their equivalents may beimplemented with computer executable instructions. Thus, in anembodiment, a non-transitory computer readable/storage medium isconfigured with stored computer executable instructions of analgorithm/executable application that when executed by a machine(s)cause the machine(s) (and/or associated components) to perform themethod. Example machines include but are not limited to a processor, acomputer, a server operating in a cloud computing system, a serverconfigured in a Software as a Service (SaaS) architecture, a smartphone, and so on). In an embodiment, a computing device is implementedwith one or more executable algorithms that are configured to performany of the disclosed methods.

It will be appreciated that processes, architectures and/or proceduresdescribed herein can be implemented in hardware, firmware and/orsoftware. It will also be appreciated that the provisions set forthherein may apply to any type of special-purpose computer (e.g., filehost, storage server and/or storage serving appliance) and/orgeneral-purpose computer, including a standalone computer or portionthereof, embodied as or including a storage system. Moreover, theteachings herein can be configured to a variety of storage systemarchitectures including, but not limited to, a network-attached storageenvironment and/or a storage area network and disk assembly directlyattached to a client or host computer. Storage system should thereforebe taken broadly to include such arrangements in addition to anysubsystems configured to perform a storage function and associated withother equipment or systems.

In some embodiments, methods described and/or illustrated in thisdisclosure may be realized in whole or in part on computer-readablemedia. Computer readable media can include processor-executableinstructions configured to implement one or more of the methodspresented herein, and may include any mechanism for storing this datathat can be thereafter read by a computer system. Examples of computerreadable media include (hard) drives (e.g., accessible via networkattached storage (NAS)), Storage Area Networks (SAN), volatile andnon-volatile memory, such as read-only memory (ROM), random-accessmemory (RAM), electrically erasable programmable read-only memory(EEPROM) and/or flash memory, compact disk read only memory (CD-ROM)s,CD-Rs, compact disk re-writeable (CD-RW)s, DVDs, cassettes, magnetictape, magnetic disk storage, optical or non-optical data storage devicesand/or any other medium which can be used to store data.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of implementing at least some of the claims.

Various operations of embodiments are provided herein. The order inwhich some or all of the operations are described should not beconstrued to imply that these operations are necessarily orderdependent. Alternative ordering will be appreciated given the benefit ofthis description. Further, it will be understood that not all operationsare necessarily present in each embodiment provided herein. Also, itwill be understood that not all operations are necessary in someembodiments.

Furthermore, the claimed subject matter is implemented as a method,apparatus, or article of manufacture using standard application orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer application accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

As used in this application, the terms “component”, “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentincludes a process running on a processor, a processor, an object, anexecutable, a thread of execution, an application, or a computer. By wayof illustration, both an application running on a controller and thecontroller can be a component. One or more components residing within aprocess or thread of execution and a component may be localized on onecomputer or distributed between two or more computers.

Moreover, “exemplary” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused in this application, “or” is intended to mean an inclusive “or”rather than an exclusive “or”. In addition, “a” and “an” as used in thisapplication are generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Also, at least one of A and B and/or the like generally means A orB and/or both A and B. Furthermore, to the extent that “includes”,“having”, “has”, “with”, or variants thereof are used, such terms areintended to be inclusive in a manner similar to the term “comprising”.

Many modifications may be made to the instant disclosure withoutdeparting from the scope or spirit of the claimed subject matter. Unlessspecified otherwise, “first,” “second,” or the like are not intended toimply a temporal aspect, a spatial aspect, an ordering, etc. Rather,such terms are merely used as identifiers, names, etc. for features,elements, items, etc. For example, a first set of information and asecond set of information generally correspond to set of information Aand set of information B or two different or two identical sets ofinformation or the same set of information.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method comprising: receiving, by a computingdevice, a message containing sensor data generated by a sensor of a datanetwork based a property of the sensor data indicating that managementof the sensor data is to be offloaded from the data network to thecomputing device; storing the sensor data within storage managed by thecomputing device; generating a handle used to identify the sensor data;replacing the sensor data within the message with the handle;transmitting the message to a device within the data network; andfacilitating access by the device to the sensor data using the handle.2. The method of claim 1, wherein the sensor data is routed from thedata network to the computing device based upon the property indicatingthat the sensor data has a size greater than a threshold.
 3. The methodof claim 1, wherein the sensor data is routed from the data network tothe computing device based upon the property indicating that the sensordata has a generation frequency greater than a threshold.
 4. The methodof claim 1, wherein the storing comprises: storing a set of data samplesof the sensor data as a single object.
 5. The method of claim 1,comprising: retrieving the sensor data from storage local to thecomputing device using the handle based upon receiving a requestcomprising the handle from the device, wherein the sensor data istransmitted to the device.
 6. The method of claim 1, comprising:identifying a remote storage location of the sensor data that is remoteto the computing device based upon receiving a request comprising thehandle from the device.
 7. The method of claim 6, comprising: retrievingthe sensor data from the remote storage location using the handle,wherein the sensor data is transmitted to the device.
 8. The method ofclaim 1, wherein the generating a handle comprises: encoding the handlewith information derived from a property of the sensor.
 9. The method ofclaim 1, wherein the generating a handle comprises: encoding the handlewith information derived from a property of the sensor data.
 10. Themethod of claim 9, wherein the handle is used to verify integrity of thesensor data.
 11. The method of claim 1, wherein the facilitating accesscomprises: representing the sensor data as a file system accessible todevices within the data network.
 12. The method of claim 1, wherein thesensor is comprised within an edge network of the data network, thedevice is comprised within a core network of the data network, and asecond device is comprised within a cloud network of the data network,wherein the computing device provides the device and the second devicewith access to the sensor data based upon the handle.
 13. The method ofclaim 12, comprising: replicating the sensor data to storage of thecloud network to create replicated sensor data accessible from thestorage of the cloud network using the handle.
 14. The method of claim12, comprising: replicating the sensor data to storage of the corenetwork to create replicated sensor data accessible from the storage ofthe core network using the handle.
 15. The method of claim 12,comprising: replicating the sensor data to storage of the edge networkto create replicated sensor data accessible from the storage of the edgenetwork using the handle.
 16. A non-transitory machine readable mediumcomprising instructions for performing a method, which when executed bya machine, causes the machine to: receive, by a computing device, amessage comprising sensor data generated by a sensor of a data networkbased a property of the sensor data indicating that management of thesensor data is to be offloaded from the data network to the computingdevice; store the sensor data within storage managed by the computingdevice; generate a handle used to identify the sensor data; replace thesensor data within the message with the handle; transmit the message toa device within the data network; and facilitate access by the device tothe sensor data using the handle.
 17. The non-transitory machinereadable medium of claim 16, wherein the instructions cause the machineto: deduplicate the sensor data based upon a deduplication policy. 18.The non-transitory machine readable medium of claim 16, wherein theinstructions cause the machine to: compress the sensor data based upon acompression policy.
 19. The non-transitory machine readable medium ofclaim 16, wherein the instructions cause the machine to: encrypt thesensor data based upon an encryption policy.
 20. A computing devicecomprising: a memory comprising machine executable code for performing amethod; and a processor coupled to the memory, the processor configuredto execute the machine executable code to cause the processor to:receive a message comprising sensor data generated by a sensor of a datanetwork based a property of the sensor data indicating that managementof the sensor data is to be offloaded from the data network to thecomputing device; store the sensor data within storage managed by thecomputing device; generate a handle used to identify the sensor data;replace the sensor data within the message with the handle; transmit themessage to a device within the data network; and facilitate access bythe device to the sensor data using the handle.